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Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3051–3100 of 3304 papers

TitleStatusHype
Adaptive Randomized Dimension Reduction on Massive Data—0
Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks—0
Maximum Entropy Linear Manifold for Learning Discriminative Low-dimensional Representation—0
Generalized Categorization Axioms—0
Cross-validation of matching correlation analysis by resampling matching weights—0
An Item-Based Collaborative Filtering using Dimensionality Reduction Techniques on Mahout Framework—0
Unsupervised model compression for multilayer bootstrap networks—0
Reduced Basis Decomposition: a Certified and Fast Lossy Data Compression Algorithm—0
Hypoelliptic Diffusion Maps I: Tangent Bundles—0
2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA—0
Functional Inverse Regression in an Enlarged Dimension Reduction Space—0
Diverse Landmark Sampling from Determinantal Point Processes for Scalable Manifold Learning—0
Scalable Iterative Algorithm for Robust Subspace Clustering—0
Puzzle Imaging: Using Large-scale Dimensionality Reduction Algorithms for Localization—0
On Convolutional Approximations to Linear Dimensionality Reduction Operators for Large Scale Data Processing—0
Sparse Multivariate Factor Regression—0
The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction—0
Personalising Mobile Advertising Based on Users Installed Apps—0
On the consistency theory of high dimensional variable screening—0
Tensor Canonical Correlation Analysis for Multi-view Dimension ReductionCode1
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines—0
Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs—0
Optimized Projection for Sparse Representation Based Classification—0
SHOE: Supervised Hashing with Output Embeddings—0
IT-map: an Effective Nonlinear Dimensionality Reduction Method for Interactive Clustering—0
Deep Learning with Nonparametric ClusteringCode0
Deep Autoencoders for Dimensionality Reduction of High-Content Screening Data—0
Chainer: a Next-Generation Open Source Framework for Deep LearningCode0
A simple coding for cross-domain matching with dimension reduction via spectral graph embedding—0
Outperforming Word2Vec on Analogy Tasks with Random Projections—0
Rehabilitation of Count-based Models for Word Vector Representations—0
Sequential Labeling with online Deep Learning—0
Probabilistic low-rank matrix completion on finite alphabets—0
Web image annotation by diffusion maps manifold learning algorithm—0
Dimensionality Reduction with Subspace Structure Preservation—0
Deep Distributed Random Samplings for Supervised Learning: An Alternative to Random Forests?—0
Subspace Embeddings for the Polynomial Kernel—0
Cone-Constrained Principal Component Analysis—0
Sparse Variational Bayesian Approximations for Nonlinear Inverse Problems: applications in nonlinear elastography—0
Metrics for Probabilistic Geometries—0
Worst-Case Linear Discriminant Analysis as Scalable Semidefinite Feasibility Problems—0
A Convex Sparse PCA for Feature Analysis—0
A Convex Formulation for Spectral Shrunk Clustering—0
Similarity Learning for High-Dimensional Sparse DataCode0
Distributed Low-Rank Estimation Based on Joint Iterative Optimization in Wireless Sensor Networks—0
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection—0
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation—0
Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parametersCode0
Where do goals come from? A Generic Approach to Autonomous Goal-System Development—0
MKL-RT: Multiple Kernel Learning for Ratio-trace Problems via Convex Optimization—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified